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import numpy as np
import librosa
import mir_eval
import torch
import os
idx2chord = ['C', 'C:min', 'C#', 'C#:min', 'D', 'D:min', 'D#', 'D#:min', 'E', 'E:min', 'F', 'F:min', 'F#',
'F#:min', 'G', 'G:min', 'G#', 'G#:min', 'A', 'A:min', 'A#', 'A#:min', 'B', 'B:min', 'N']
root_list = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
quality_list = ['min', 'maj', 'dim', 'aug', 'min6', 'maj6', 'min7', 'minmaj7', 'maj7', '7', 'dim7', 'hdim7', 'sus2', 'sus4']
def idx2voca_chord():
idx2voca_chord = {}
idx2voca_chord[169] = 'N'
idx2voca_chord[168] = 'X'
for i in range(168):
root = i // 14
root = root_list[root]
quality = i % 14
quality = quality_list[quality]
if i % 14 != 1:
chord = root + ':' + quality
else:
chord = root
idx2voca_chord[i] = chord
return idx2voca_chord
def audio_file_to_features(audio_file, config):
original_wav, sr = librosa.load(audio_file, sr=config.mp3['song_hz'], mono=True)
currunt_sec_hz = 0
while len(original_wav) > currunt_sec_hz + config.mp3['song_hz'] * config.mp3['inst_len']:
start_idx = int(currunt_sec_hz)
end_idx = int(currunt_sec_hz + config.mp3['song_hz'] * config.mp3['inst_len'])
tmp = librosa.cqt(original_wav[start_idx:end_idx], sr=sr, n_bins=config.feature['n_bins'], bins_per_octave=config.feature['bins_per_octave'], hop_length=config.feature['hop_length'])
if start_idx == 0:
feature = tmp
else:
feature = np.concatenate((feature, tmp), axis=1)
currunt_sec_hz = end_idx
tmp = librosa.cqt(original_wav[currunt_sec_hz:], sr=sr, n_bins=config.feature['n_bins'], bins_per_octave=config.feature['bins_per_octave'], hop_length=config.feature['hop_length'])
feature = np.concatenate((feature, tmp), axis=1)
feature = np.log(np.abs(feature) + 1e-6)
feature_per_second = config.mp3['inst_len'] / config.model['timestep']
song_length_second = len(original_wav)/config.mp3['song_hz']
return feature, feature_per_second, song_length_second
# Audio files with format of wav and mp3
def get_audio_paths(audio_dir):
return [os.path.join(root, fname) for (root, dir_names, file_names) in os.walk(audio_dir, followlinks=True)
for fname in file_names if (fname.lower().endswith('.wav') or fname.lower().endswith('.mp3'))]
def get_lab_paths(lab_dir):
return [os.path.join(root, fname) for (root, dir_names, file_names) in os.walk(lab_dir, followlinks=True)
for fname in file_names if (fname.lower().endswith('.lab'))]
class metrics():
def __init__(self):
super(metrics, self).__init__()
self.score_metrics = ['root', 'thirds', 'triads', 'sevenths', 'tetrads', 'majmin', 'mirex']
self.score_list_dict = dict()
for i in self.score_metrics:
self.score_list_dict[i] = list()
self.average_score = dict()
def score(self, metric, gt_path, est_path):
if metric == 'root':
score = self.root_score(gt_path,est_path)
elif metric == 'thirds':
score = self.thirds_score(gt_path,est_path)
elif metric == 'triads':
score = self.triads_score(gt_path,est_path)
elif metric == 'sevenths':
score = self.sevenths_score(gt_path,est_path)
elif metric == 'tetrads':
score = self.tetrads_score(gt_path,est_path)
elif metric == 'majmin':
score = self.majmin_score(gt_path,est_path)
elif metric == 'mirex':
score = self.mirex_score(gt_path,est_path)
else:
raise NotImplementedError
return score
def root_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.root(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def thirds_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.thirds(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def triads_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.triads(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def sevenths_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.sevenths(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def tetrads_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.tetrads(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def majmin_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.majmin(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def mirex_score(self, gt_path, est_path):
(ref_intervals, ref_labels) = mir_eval.io.load_labeled_intervals(gt_path)
ref_labels = lab_file_error_modify(ref_labels)
(est_intervals, est_labels) = mir_eval.io.load_labeled_intervals(est_path)
est_intervals, est_labels = mir_eval.util.adjust_intervals(est_intervals, est_labels, ref_intervals.min(),
ref_intervals.max(), mir_eval.chord.NO_CHORD,
mir_eval.chord.NO_CHORD)
(intervals, ref_labels, est_labels) = mir_eval.util.merge_labeled_intervals(ref_intervals, ref_labels,
est_intervals, est_labels)
durations = mir_eval.util.intervals_to_durations(intervals)
comparisons = mir_eval.chord.mirex(ref_labels, est_labels)
score = mir_eval.chord.weighted_accuracy(comparisons, durations)
return score
def lab_file_error_modify(ref_labels):
for i in range(len(ref_labels)):
if ref_labels[i][-2:] == ':4':
ref_labels[i] = ref_labels[i].replace(':4', ':sus4')
elif ref_labels[i][-2:] == ':6':
ref_labels[i] = ref_labels[i].replace(':6', ':maj6')
elif ref_labels[i][-4:] == ':6/2':
ref_labels[i] = ref_labels[i].replace(':6/2', ':maj6/2')
elif ref_labels[i] == 'Emin/4':
ref_labels[i] = 'E:min/4'
elif ref_labels[i] == 'A7/3':
ref_labels[i] = 'A:7/3'
elif ref_labels[i] == 'Bb7/3':
ref_labels[i] = 'Bb:7/3'
elif ref_labels[i] == 'Bb7/5':
ref_labels[i] = 'Bb:7/5'
elif ref_labels[i].find(':') == -1:
if ref_labels[i].find('min') != -1:
ref_labels[i] = ref_labels[i][:ref_labels[i].find('min')] + ':' + ref_labels[i][ref_labels[i].find('min'):]
return ref_labels
def root_majmin_score_calculation(valid_dataset, config, mean, std, device, model, model_type, verbose=False):
valid_song_names = valid_dataset.song_names
paths = valid_dataset.preprocessor.get_all_files()
metrics_ = metrics()
song_length_list = list()
for path in paths:
song_name, lab_file_path, mp3_file_path, _ = path
if not song_name in valid_song_names:
continue
try:
n_timestep = config.model['timestep']
feature, feature_per_second, song_length_second = audio_file_to_features(mp3_file_path, config)
feature = feature.T
feature = (feature - mean) / std
time_unit = feature_per_second
num_pad = n_timestep - (feature.shape[0] % n_timestep)
feature = np.pad(feature, ((0, num_pad), (0, 0)), mode="constant", constant_values=0)
num_instance = feature.shape[0] // n_timestep
start_time = 0.0
lines = []
with torch.no_grad():
model.eval()
feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0).to(device)
for t in range(num_instance):
if model_type == 'btc':
encoder_output, _ = model.self_attn_layers(feature[:, n_timestep * t:n_timestep * (t + 1), :])
prediction, _ = model.output_layer(encoder_output)
prediction = prediction.squeeze()
elif model_type == 'cnn' or model_type =='crnn':
prediction, _, _, _ = model(feature[:, n_timestep * t:n_timestep * (t + 1), :], torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
for i in range(n_timestep):
if t == 0 and i == 0:
prev_chord = prediction[i].item()
continue
if prediction[i].item() != prev_chord:
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2chord[prev_chord]))
start_time = time_unit * (n_timestep * t + i)
prev_chord = prediction[i].item()
if t == num_instance - 1 and i + num_pad == n_timestep:
if start_time != time_unit * (n_timestep * t + i):
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2chord[prev_chord]))
break
pid = os.getpid()
tmp_path = 'tmp_' + str(pid) + '.lab'
with open(tmp_path, 'w') as f:
for line in lines:
f.write(line)
root_majmin = ['root', 'majmin']
for m in root_majmin:
metrics_.score_list_dict[m].append(metrics_.score(metric=m, gt_path=lab_file_path, est_path=tmp_path))
song_length_list.append(song_length_second)
if verbose:
for m in root_majmin:
print('song name %s, %s score : %.4f' % (song_name, m, metrics_.score_list_dict[m][-1]))
except:
print('song name %s\' lab file error' % song_name)
tmp = song_length_list / np.sum(song_length_list)
for m in root_majmin:
metrics_.average_score[m] = np.sum(np.multiply(metrics_.score_list_dict[m], tmp))
return metrics_.score_list_dict, song_length_list, metrics_.average_score
def root_majmin_score_calculation_crf(valid_dataset, config, mean, std, device, pre_model, model, model_type, verbose=False):
valid_song_names = valid_dataset.song_names
paths = valid_dataset.preprocessor.get_all_files()
metrics_ = metrics()
song_length_list = list()
for path in paths:
song_name, lab_file_path, mp3_file_path, _ = path
if not song_name in valid_song_names:
continue
try:
n_timestep = config.model['timestep']
feature, feature_per_second, song_length_second = audio_file_to_features(mp3_file_path, config)
feature = feature.T
feature = (feature - mean) / std
time_unit = feature_per_second
num_pad = n_timestep - (feature.shape[0] % n_timestep)
feature = np.pad(feature, ((0, num_pad), (0, 0)), mode="constant", constant_values=0)
num_instance = feature.shape[0] // n_timestep
start_time = 0.0
lines = []
with torch.no_grad():
model.eval()
feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0).to(device)
for t in range(num_instance):
if (model_type == 'cnn') or (model_type == 'crnn') or (model_type == 'btc'):
logits = pre_model(feature[:, n_timestep * t:n_timestep * (t + 1), :], torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
prediction, _ = model(logits, torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
else:
raise NotImplementedError
for i in range(n_timestep):
if t == 0 and i == 0:
prev_chord = prediction[i].item()
continue
if prediction[i].item() != prev_chord:
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2chord[prev_chord]))
start_time = time_unit * (n_timestep * t + i)
prev_chord = prediction[i].item()
if t == num_instance - 1 and i + num_pad == n_timestep:
if start_time != time_unit * (n_timestep * t + i):
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2chord[prev_chord]))
break
pid = os.getpid()
tmp_path = 'tmp_' + str(pid) + '.lab'
with open(tmp_path, 'w') as f:
for line in lines:
f.write(line)
root_majmin = ['root', 'majmin']
for m in root_majmin:
metrics_.score_list_dict[m].append(metrics_.score(metric=m, gt_path=lab_file_path, est_path=tmp_path))
song_length_list.append(song_length_second)
if verbose:
for m in root_majmin:
print('song name %s, %s score : %.4f' % (song_name, m, metrics_.score_list_dict[m][-1]))
except:
print('song name %s\' lab file error' % song_name)
tmp = song_length_list / np.sum(song_length_list)
for m in root_majmin:
metrics_.average_score[m] = np.sum(np.multiply(metrics_.score_list_dict[m], tmp))
return metrics_.score_list_dict, song_length_list, metrics_.average_score
def large_voca_score_calculation(valid_dataset, config, mean, std, device, model, model_type, verbose=False):
idx2voca = idx2voca_chord()
valid_song_names = valid_dataset.song_names
paths = valid_dataset.preprocessor.get_all_files()
metrics_ = metrics()
song_length_list = list()
for path in paths:
song_name, lab_file_path, mp3_file_path, _ = path
if not song_name in valid_song_names:
continue
try:
n_timestep = config.model['timestep']
feature, feature_per_second, song_length_second = audio_file_to_features(mp3_file_path, config)
feature = feature.T
feature = (feature - mean) / std
time_unit = feature_per_second
num_pad = n_timestep - (feature.shape[0] % n_timestep)
feature = np.pad(feature, ((0, num_pad), (0, 0)), mode="constant", constant_values=0)
num_instance = feature.shape[0] // n_timestep
start_time = 0.0
lines = []
with torch.no_grad():
model.eval()
feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0).to(device)
for t in range(num_instance):
if model_type == 'btc':
encoder_output, _ = model.self_attn_layers(feature[:, n_timestep * t:n_timestep * (t + 1), :])
prediction, _ = model.output_layer(encoder_output)
prediction = prediction.squeeze()
elif model_type == 'cnn' or model_type =='crnn':
prediction, _, _, _ = model(feature[:, n_timestep * t:n_timestep * (t + 1), :], torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
for i in range(n_timestep):
if t == 0 and i == 0:
prev_chord = prediction[i].item()
continue
if prediction[i].item() != prev_chord:
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2voca[prev_chord]))
start_time = time_unit * (n_timestep * t + i)
prev_chord = prediction[i].item()
if t == num_instance - 1 and i + num_pad == n_timestep:
if start_time != time_unit * (n_timestep * t + i):
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2voca[prev_chord]))
break
pid = os.getpid()
tmp_path = 'tmp_' + str(pid) + '.lab'
with open(tmp_path, 'w') as f:
for line in lines:
f.write(line)
for m in metrics_.score_metrics:
metrics_.score_list_dict[m].append(metrics_.score(metric=m, gt_path=lab_file_path, est_path=tmp_path))
song_length_list.append(song_length_second)
if verbose:
for m in metrics_.score_metrics:
print('song name %s, %s score : %.4f' % (song_name, m, metrics_.score_list_dict[m][-1]))
except:
print('song name %s\' lab file error' % song_name)
tmp = song_length_list / np.sum(song_length_list)
for m in metrics_.score_metrics:
metrics_.average_score[m] = np.sum(np.multiply(metrics_.score_list_dict[m], tmp))
return metrics_.score_list_dict, song_length_list, metrics_.average_score
def large_voca_score_calculation_crf(valid_dataset, config, mean, std, device, pre_model, model, model_type, verbose=False):
idx2voca = idx2voca_chord()
valid_song_names = valid_dataset.song_names
paths = valid_dataset.preprocessor.get_all_files()
metrics_ = metrics()
song_length_list = list()
for path in paths:
song_name, lab_file_path, mp3_file_path, _ = path
if not song_name in valid_song_names:
continue
try:
n_timestep = config.model['timestep']
feature, feature_per_second, song_length_second = audio_file_to_features(mp3_file_path, config)
feature = feature.T
feature = (feature - mean) / std
time_unit = feature_per_second
num_pad = n_timestep - (feature.shape[0] % n_timestep)
feature = np.pad(feature, ((0, num_pad), (0, 0)), mode="constant", constant_values=0)
num_instance = feature.shape[0] // n_timestep
start_time = 0.0
lines = []
with torch.no_grad():
model.eval()
feature = torch.tensor(feature, dtype=torch.float32).unsqueeze(0).to(device)
for t in range(num_instance):
if (model_type == 'cnn') or (model_type == 'crnn') or (model_type == 'btc'):
logits = pre_model(feature[:, n_timestep * t:n_timestep * (t + 1), :], torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
prediction, _ = model(logits, torch.randint(config.model['num_chords'], (n_timestep,)).to(device))
else:
raise NotImplementedError
for i in range(n_timestep):
if t == 0 and i == 0:
prev_chord = prediction[i].item()
continue
if prediction[i].item() != prev_chord:
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2voca[prev_chord]))
start_time = time_unit * (n_timestep * t + i)
prev_chord = prediction[i].item()
if t == num_instance - 1 and i + num_pad == n_timestep:
if start_time != time_unit * (n_timestep * t + i):
lines.append(
'%.6f %.6f %s\n' % (
start_time, time_unit * (n_timestep * t + i), idx2voca[prev_chord]))
break
pid = os.getpid()
tmp_path = 'tmp_' + str(pid) + '.lab'
with open(tmp_path, 'w') as f:
for line in lines:
f.write(line)
for m in metrics_.score_metrics:
metrics_.score_list_dict[m].append(metrics_.score(metric=m, gt_path=lab_file_path, est_path=tmp_path))
song_length_list.append(song_length_second)
if verbose:
for m in metrics_.score_metrics:
print('song name %s, %s score : %.4f' % (song_name, m, metrics_.score_list_dict[m][-1]))
except:
print('song name %s\' lab file error' % song_name)
tmp = song_length_list / np.sum(song_length_list)
for m in metrics_.score_metrics:
metrics_.average_score[m] = np.sum(np.multiply(metrics_.score_list_dict[m], tmp))
return metrics_.score_list_dict, song_length_list, metrics_.average_score
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